ArticlePloS one2025
The complex impact of China's science and technology talent policies on key core technologies R&D.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Key core technologies have become the focal point of global technological competition. For latecomer countries, formulating effective science and technology talent policies to stimulate researchers' motivation and innovation potential is crucial for mastering these key technologies. This study uses a sample of 1363 publicly listed enterprises in China from 2011 to 2023 to investigate the impact of science and technology talent policies on key core technologies R&D in enterprises. The findings are as follows: (1) Science and technology talent policies significantly promote key core technologies R&D; however, their impact is stronger on non-key core technologies R&D and utility model R&D than on key core technologies R&D. Further study found that non-key core technologies R&D and utility model R&D have a crowding-out effect on key core technologies R&D to a certain extent, and the effect of the former is stronger than that of the latter. (2) As the intensity of policy support increases, its impact on key core technologies R&D also strengthens. Moreover, policies related to talent subsidies, innovation incentives, talent introduction, and high-level talent innovation support all facilitate key core technologies R&D, although the effects vary across these policy types. Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. The study demonstrates that latecomer countries can effectively promote key core technologies R&D by designing science and technology talent policies. Based on these findings, recommendations are made to enhance the support intensity of talent policies, optimize the synergistic effects of these policies, extend the duration of policy support, and implement differentiated measures for policy execution.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.